Deep learning in spiking neural networks
Amirhossein Tavanaei,Masoud Ghodrati,Saeed Reza Kheradpisheh,Timothée Masquelier,Anthony S. Maida +4 more
TLDR
The emerging picture is that SNNs still lag behind ANNs in terms of accuracy, but the gap is decreasing, and can even vanish on some tasks, while SNN's typically require many fewer operations and are the better candidates to process spatio-temporal data.About:
This article is published in Neural Networks.The article was published on 2019-03-01 and is currently open access. It has received 756 citations till now. The article focuses on the topics: Spiking neural network & Artificial neural network.read more
Citations
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Proceedings ArticleDOI
Rethinking Benchmarks for Neuromorphic Learning Algorithms
Qu Yang,Jibin Wu,Haizhou Li +2 more
TL;DR: In this paper, a spatio-temporal learning framework is proposed to selectively control the information flow along both spatial and temporal directions during feedforward and backward propagation of neural networks.
Journal ArticleDOI
Brain-Inspired Spiking Neural Network Controller for a Neurorobotic Whisker System
TL;DR: In this paper , a bioinspired spiking neural network model of the mouse whisker system was developed, which was embedded in a virtual mouse robot, exploiting the Human Brain Project's Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by braininspired controllers.
Proceedings ArticleDOI
Artificial Neuron using MoS 2 /Graphene Threshold Switching Memristors
Hirokjyoti Kalita,Adithi Krishnaprasad,Nitin Choudhary,Sonali Das,Hee-Suk Chung,Yeonwoong Jung,Tania Roy +6 more
TL;DR: In this paper, the authors used the volatility of threshold switching MoS 2 /Graphene (Gr) 2D/2D heterojunction system to realize the integration-and-fire response of a neuron.
Journal ArticleDOI
Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data
TL;DR: In this paper , the authors explore backdoor triggers within neuromorphic data that can manipulate their position and color, providing a broader scope of possibilities than conventional triggers in domains like images, achieving an attack success rate of up to 100% while maintaining a negligible impact on clean accuracy.
Journal ArticleDOI
Stochasticity in the synchronization of strongly coupled spiking oscillators
Erbin Qiu,Pavel Salev,L. Fratino,R. Rocco,H. Navarro,Coline Adda,Junjie Li,Min-Han Lee,Yoav Kalcheim,Marcelo J. Rozenberg,Ivan K. Schuller +10 more
TL;DR: In this paper , the authors report the emergence of an unusual stochastic pattern in coupled spiking Mott nanodevices, which leads to a discrete inter-spike interval distribution similar to those in biological neurons.
References
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
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